Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Association rules over interval data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
A statistical theory for quantitative association rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Mining Frequent Item Sets with Convertible Constraints
Proceedings of the 17th International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Efficient Mining of Constrained Correlated Sets
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Carpenter: finding closed patterns in long biological datasets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
On the discovery of significant statistical quantitative rules
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Frequent Itemset Mining Using FP-Trees
IEEE Transactions on Knowledge and Data Engineering
Efficient aggregate licenses validation in DRM
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part II
Fast tree-based mining of frequent itemsets from uncertain data
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
Aggregate licenses validation for digital rights violation detection
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) - Special Issue on Multimedia Security
Computation time efficient approach for licenses validation in DRM systems
Multimedia Tools and Applications
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Frequent pattern mining (FPM) has become one of the most popular data mining approaches for the analysis of purchasing patterns. Methods such as Apriori and FP-growth have been shown to work efficiently in this setting. However, these techniques are typically restricted to a single concept level. Since typical business databases support hierarchies that represent the relationships amongst many different concept levels, it is important that we extend our focus to discover frequent patterns in multi-level environments. Unfortunately, little attention has been paid to this research area. In this paper, we present two novel algorithms that efficiently discover multi-level frequent patterns. Adopting either a top-down or bottom-up approach, our algorithms exploit existing fp-tree structures, rather than excessively scanning the raw data set multiple times, as might be done with a naive implementation. In addition, we also introduce an algorithm to mine cross-level frequent patterns. Experimental results have shown that our new algorithms maintain their performance advantage across a broad spectrum of test environments.